(1) Field of the Invention
The present invention relates to a detecting device for road monitoring, and particularly to a detecting device for road monitoring which detects, based on image information obtained from a television camera provided in the vicinity of a road, traveling states of vehicles on a road (traffic stagnation, stopping of vehicles, etc.) or fallen objects on a road.
(2) Description of the Related Art
For the sake of safety, it is important that a road supervisor should quickly detect, on a road, traffic stagnation, stopping of vehicles due to a traffic accident, existence of an object fallen from a vehicle, and call attention of succeeding vehicles and/or take suitable measures. This is particularly important on a highway. Since detection of such states needs to be constantly and broadly conducted, it is desirable that it can be automatically conducted without requiring human hands.
FIG. 10 shows an example of formation of a conventional detecting device for road monitoring. A black-and-white television camera for picking up images of a road is provided in the vicinity of a road. An image taken by the television camera is inputted through a video board to a mask/background/difference processing section 101. The television camera is so positioned that it may watch vehicles traveling on one side of the road from the rear. The mask/background/difference processing section 101 first conducts a mask processing of inputted image data (luminance information) in order to cut out only an image section being processed. Generally, since processing is performed for each lane of a road, an image section corresponding to one lane is cut out. An average of luminance of each picture element constituting the cut-out image section in a first predetermined period of time (for example, from several to over ten seconds) is obtained, thereby to prepare background image data which corresponds to a state of road having no object to be detected (hereinafter "object to be detected" will be expressed as "detection object".). Specifically, the inputted image data is luminance information for each picture element, where each picture element has a value of luminance in 256 gradations. A value of luminance of a picture element in the inputted image data is compared with a value of luminance of a corresponding picture element in the prepared background image data, and if the former is larger than the latter, a value of luminance of that picture element in the background image data is increased by one gradation, and if the former is smaller than the latter, a value of luminance of that picture element in the background image data is reduced by one gradation. Such processing is repeated for the aforementioned first predetermined period of time, so that background image data including hardly any image element of a detection object, therefore, representing almost only the road surface can be obtained.
The mask/background/difference processing section 101 obtains difference in luminance for each picture element between the newly inputted image data and the prepared background image data, and compares the absolute value of the obtained difference in luminance for each picture element (in 256 gradations) with a predetermined threshold value. If the absolute value of the difference in luminance obtained for a picture element exceeds the threshold value, value 1 is given to that picture element. If not, value 0 is given to that picture element. In thus obtained binary data, an image made of picture elements having value 1 is considered as an image of a detected object which is not included in the background image data. The threshold value is a fixed value.
A stagnation extracting section 102 receives the aforementioned binary data from the mask/background/difference processing section 101, and in order to facilitate extraction of features of vehicles, detects edge portions of vehicles and obtains their values projected in Y direction (approximately the traveling direction of vehicles). The obtained projected values are fed to a judging section 104.
A stationary object extracting section 103 receives the aforementioned background image data from the mask/background/difference processing section 101 and prepares second background image data. As mentioned above, the first background image data is prepared by taking an average of the inputted image data in the first predetermined period of time. The second background image data is prepared by taking an average of the first background image data in a second predetermined period of time (for example, several minutes) which is longer than the first predetermined period of time. Then, the stationary object extracting section 103 obtains difference in luminance for each picture element between the first background image data and the second background image data, compares the absolute value of the obtained difference for each picture element (in 256 gradations) with a predetermined threshold value, and obtains the result of comparison as binary data. Thus obtained binary data is considered to represent an image of a vehicle stopping on the road and/or an image of an object fallen from a vehicle. Even if an image of a stopping vehicle and/or a fallen object is included in the first background image data which is obtained by taking an average in the shorter first predetermined period of time, such an image may disappear from the second background image data which is obtained by taking an average in the longer second predetermined period of time. Therefore, when difference between the first and second background image data is obtained, it may reveal an image of a stopping vehicle and/or a fallen object.
A judging section 104 detects individual vehicles based on the aforementioned projected values fed from the stagnation extracting section 102, and calculates the number of vehicles and the velocity of the rearmost vehicle. Based on the calculated number and velocity, the judging section 104 detects a state of traffic stagnation. The judging section 104 further detects existence of a stopping vehicle and/or an object fallen from a vehicle, based on the aforementioned binary data fed from the stationary object extracting section 103.
Based on the result of judgment by the judging section 104, a monitoring display 105 provides indication of traffic stagnation, stopping of vehicles and/or existence of fallen objects.
When it is in the normal daytime and luminance of a road surface is constant, omission of detection and false detection are relatively rare even with the conventional detecting device for road monitoring. However, in the nighttime or in such daytime that luminance of a road surface varies due to movement of clouds, omission and false detection increase. Three cases in which omission and false detection increase will be explained hereunder.
(a) The case in which a vehicle is to be detected in the nighttime.
With the conventional detecting device for road monitoring, a road surface lightened by headlights of a traveling vehicle is falsely judged to be a vehicle. Therefore, it is falsely assumed that a vehicle exists in the front of a traveling vehicle, in a monitored lane into which a traveling vehicle is coming, or in a monitored lane next to a traveling vehicle, though there is actually no vehicle there.
(b) The case in which a fallen object or a stopping vehicle with its taillights put out is to be detected in the nighttime on a road surface provided with no lighting.
Since there is very little difference in luminance between an image of a detection object and its background, omission of detection occurs. If the aforementioned threshold value is lowered, such omission may be prevented. However, due to the lowered threshold value, false detection may increase in the daytime.
(c) The case in which a vehicle or a stationary object is to be detected in the daytime.
When a distinct shadow of a cloud is formed on a road surface and the shadow is moving, that shadow is falsely judged to be a vehicle. In the cloudy daytime and in the morning and evening, difference in luminance between an image of a detection object and its background reduces, so that possibility of omission of detection increases. For example, a vehicle having nearly the same luminance as that of the road surface may fail to be detected.